Session S1ERecommender: Helping Viewers in their Choice for   Educational Programs in Digital TV Context                  ...
Session S1Etransmitted through TV newscasts is the biggest link               features anymore due particularly to the fin...
Session S1Ebesides the profile combination, the time and day of the          a set of modules responsible for the data pro...
Session S1E                                                                items. For example, the system can be used to c...
Session S1EA new table was created, identical to the EPG table, butadded with fields containing the genres names. Accordin...
Session S1E                                                                             [3]    Souza Filho, G. L., Leite, ...
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Fie recommender helping viewers in their choice for educational programs in digital tv context

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Currently in Brazil, a fundamental change is
taking place in TV: the migration from analogue to
digital TV system. This change has two main
implications: an increase in transmission capacity for
new channels with the same bandwidth and the ability to
send applications with multiplexed audio-visual content.
Brazilian government aims to exploit the transmission
capacity for new channels offering programming created
to distance learning and thereby promoting social
inclusion in the vast majority of the population. This
information overload demands mechanisms to help
students to browse and select what education programs
are best suited to their current level. Personalized
recommendation systems emerge as a solution to this
problem, providing the viewer with educational
programs relevant to his profile. In this paper we present
a personalized recommendation system, the
Recommender consistent with the reference
implementation of the Brazilian digital TV system.
Finally, we present the results obtained after using the
proposed system.

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Fie recommender helping viewers in their choice for educational programs in digital tv context

  1. 1. Session S1ERecommender: Helping Viewers in their Choice for Educational Programs in Digital TV Context Paulo Muniz de Ávila, Elaine Cecília Gatto, Sergio Donizetti Zorzo pauloavila @pucpocos.com.br,elaine_gatto@dc.ufscar.br,zorzo@dc.ufscar.brAbstract - Currently in Brazil, a fundamental change is favorite program. In face of this situation, personalizedtaking place in TV: the migration from analogue to recommendation systems are necessary.digital TV system. This change has two main Different from EPG functions which allow basic search,implications: an increase in transmission capacity for a personalized TV system can create a profile for each TVnew channels with the same bandwidth and the ability to viewer and recommend programs that best match thissend applications with multiplexed audio-visual content. profile, avoiding the search in many EPG options to find theBrazilian government aims to exploit the transmission favorite program. Elementary and secondary educationcapacity for new channels offering programming created schools and universities generally seek to explore this newto distance learning and thereby promoting social model offering personalized content to their students. In thisinclusion in the vast majority of the population. This context, a recommendation system is able to analyze theinformation overload demands mechanisms to help profile of a group of students, suggesting the educationalstudents to browse and select what education programs content that best suits the needs of the group.are best suited to their current level. Personalized To make the benefits (new channels, interactiverecommendation systems emerge as a solution to this applications) offered by the digital system possible, the TVproblem, providing the viewer with educational viewers with analogical system need new equipment calledprograms relevant to his profile. In this paper we present set-top box (STB). STB is a device which works connecteda personalized recommendation system, the to the TV and converts the digital sign received from theRecommender consistent with the reference provider to audio/video that the analogical TV can exhibit.implementation of the Brazilian digital TV system. To have the advantages offered by the digital TV, the STBFinally, we present the results obtained after using the needs a software layer which connects the hardware to theproposed system. interactive applications called middleware. The DTV Brazilian System middleware is Ginga [2,3]. It allowsKey-words - Personalization, Multimedia, Recommendation declarative and procedural applications through itsSystem, Digital TV, Middleware Ginga. components Ginga-NCL [2] and Ginga-J [3]. Ginga-NCL performs declarative application written in Nested Context INTRODUCTION Language (NCL) while Ginga-J can perform procedural Digital television has created new services, products, application based on JavaTM known as Xlets [4].contents, channels and business models. The Brazilian This paper proposes an extension to Ginga middlewareDigital TV System allows high quality audio and video, as through implementation of a new module incorporated towell as interactivity, creating different contents for users. Ginga Common Core called Recommender. TheThere are two main implications with Brazil Digital TV Recommender module is responsible for gathering, storing,System: the increase of the number of channels being processing and recommending TV education programs. Tobroadcasted with the same bandwidth and the possibility of develop the Recommender module, Ginga-NCL middlewaresending multiplexed applications with the audio-visual developed by PUC-RIO (Pontifical Catholic University ofcontent. As new channels emerge due to the transmission Rio de Janeiro) was used, implemented in C/C++ languageincrease, it is necessary to create ways that allow the TV with source code available under GPLv2 license andviewers to search among these channels. according with the patterns defined by the Brazilian system The Electronic Program Guide (EPG) helps the TV digital television [4].viewers. However, as new channels are available, an TVDI IN BRAZIL AND EDUCATIONinformation overload is unavoidable making the EPG systeminappropriate. In Shangai [1], a big city in China, the TV One of the reasons to implement TVDi in the nationaloperators provide different services (in the analogical territory is its potential to social inclusion. In Brazil, in manysystem, channels), and this number has been increasing at a cases, the open TV is the only source of information for20% rate per year. Thus, the traditional EPG system became people who do not frequently read newspaper, magazine orunattractive because it takes too long for the viewers to any other kind of printed media. If we consider that thesearch among hundreds of options available to find their access to written information is low and that the information978-1-4244-6262-9/10/$26.00 ©2010 IEEE October 27 - 30, 2010, Washington, DC 40th ASEE/IEEE Frontiers in Education Conference S1E-1
  2. 2. Session S1Etransmitted through TV newscasts is the biggest link features anymore due particularly to the financial survival ofbetween the world and the daily routine of Brazilian people, theses broadcastings. It is possible to note, according towe have many reasons not to ignore the reach power of this legislation, that the programming only admits transmissiontechnology. If it is correctly and consciously explored, with of programs with educative-cultural purposes. However,the help of interactive resources, TVDi can represent a there is the option to recreational, informative or sportpowerful tool to have access to differentiated educational programs considered educative-cultural since they presentknowledge at the same time it can include Brazilian citizens instructive elements or educative-cultural focus identified indigitally excluded nowadays. Thus, it can be said that in its presentation.Brazil, the access to the Internet is low and high-class people Digital TV implantation in Brazil has been advancing. Someare those who have more access to it and participate obstacles – among them the situation of commercialsomehow in the educational scenario. The low number of broadcastings, political interests, influences (and models) ofpersonal computers and the high number of TV sets in digital television international systems, legislation ruling theBrazilian houses defend the efforts to use all TVDi potential radio broadcasting – still prevent its complete operation, butin issues in the educational extent. If public policies are well when it is defined, a social participation never seen before instructured, TVDi can reinforce a new educational paradigm, other historical moments can take place in Brazil, ensuringallowing the entire population to have access to Internet access to information and culture. [6]resources, video, images, sounds, interactivity to introducenew knowledge, entertainment, education, leisure, services. RELATED WORKSIt can allow the unlimited access to written and audiovisual There are several recommendation systems for DTV (Digitalinformation. As the great part of Brazilian population has a Television) designed to offer a distinct personalizationlimited access to information and Internet, and considering service and to help TV viewers to deal with the greatthe fact that the TV is the durable good which is in almost all quantity of TV programs. Some systems related to theBrazilian houses, we can consider the TVDi a way to current work are presented here.significantly change the perspective of Brazilian distance The AIMED system proposed by [7], presents alearning. Even knowing that the TVDi inclusion in Brazil recommendation mechanism that considers some TV viewerwill not solve the social inclusion problem, it is certain that characteristics as activities, interests, mood, TV useall its power can improve the digital inclusion, for it will background and demographic information. These data areensure the information access, services and education to inserted in a neural network model that infers the viewers’people with low purchasing power. [5] preferences about the programs. Unlike the work proposed EDUCATIVE BROADCASTING IN BRAZIL in this paper, which uses the implicit data collection, in the AIMED system, the data are collected and the system is setAccording to the Communication Department, educative trough questionnaires. This approach is doubtful, mainlybroadcasting is the Sound Broadcasting Service (radio) or when limitations imposed to data input in a DTV system areSounds and Images Services (TV) intended for the considered.transmission of educative-cultural programs which, besides In [8] a method to discover models of multiuser environmentperforming together with teaching systems of any level or in intelligent houses based on users’ implicit interactions ismodality, aims the basic and higher education, the presented. This method stores information in logs. So, thepermanent education and the professional education, besides logs can be used by a recommendation system in order tocomprehending educational, cultural, pedagogical and decrease effort and adapt the content for each TV viewer asprofessional orientation activities. The execution of well as for multiuser situations. Evaluating the TV viewers’broadcasting services with exclusively educative purposes is background of 20 families, it was possible to see that thegranted to legal entities with internal public right, including accuracy of the proposed model was similar to an explicituniversities, which will be given the preference to obtain the system. This shows that collecting the data in an implicitgrant, and foundations privately established and others way is as efficient as the explicit approach. In this system,Brazilian universities. the user has to identify himself in an explicit way, using theThe first educative broadcasting station, the University TV remote control. Unlike this system, the proposal in this paperof Pernambuco pertaining to the Education Department, was aims at promoting services to the recommendation systemson TV in 1967. Until 1980´s, educative TV broadcasting in for a totally implicit multiuser environment.Brazil gave priority to essentially educative programs and in In [9], a program recommendation strategy for multiple TV1997, the Brazilian Association of Public, Educative and viewers is proposed based on the combination of theCultural Broadcasting (ABEPEC) was created. In 1999, the viewer’s profile. The research analyzed three strategies toparticipant broadcastings created the RPTV (Public TV perform the content recommendation and provided theNetwork) which aims at establishing a common and choice of the strategy based on the profile combination. Themandatory programming guide to the associated results proved that the TV viewers’ profile combination canbroadcastings. Today, the programming is different from reflect properly in the preferences of the majority of thethat one in the beginning of educative broadcasting members in a group. The proposal in this paper uses antransmissions, that is, it does not have the strict educative approach similar to a multiuser environment, however,978-1-4244-6262-9/10/$26.00 ©2010 IEEE October 27 - 30, 2010, Washington, DC 40th ASEE/IEEE Frontiers in Education Conference S1E-2
  3. 3. Session S1Ebesides the profile combination, the time and day of the a set of modules responsible for the data processing,week are also considered. information filtering in the transport stream. It is theIn [1] a personalized TV system is proposed loaded in the architecture core; Stack protocol layer responsible forSTB compatible with the Multimidia Home Plataform supporting many communication protocols like HTTP, RTP(MHP) model of the digital television European pattern. and TS.According to the authors, the system was implemented in acommercial solution of the MHP middleware, and for that,implemented alterations and inclusions of new modules inthis middleware. Offering recommendation in this systemrequires two important information that must be available:programs description and the viewer visualization behavior.The description of the programs is obtained bydemultiplexing and decoding the information in the EIT(Event Information Table) table. EIT is the table used totransport specific information about programs, such as: starttime, duration and description of programs in digitaltelevision environments. The viewing behavior is collectedmonitoring the user action with the STB and the laterpersistence of this information in the STB. The work of [1]is similar to the work proposed in this paper. The implicitcollection of data, along with the inclusion of a new module FIGURE 1 – GINGA MIDDLEWARE ARCHITECTURE (ADJUSTEDin the middleware architecture, is an example of this WITH THE RECOMMENDATION SYSTEM)similarity.In [10], the Personalized Electronic Program Guide is The proposed system extends the Ginga middlewareconsidered a possible solution to the information overload functionalities including new services in the Ginga Commonproblem, mentioned in the beginning of this work. The Core layer. The Recommender module is the main part ofauthors compared the use of explicit and implicit profile and the recommendation system and it is inserted in theproved that the indicators of implicit interests are similar to Common Core layer of Ginga-NCL architecture. Thethe indicators of explicit interests. The approach to find out Recommender module is divided in two parts. The first onethe user’s profile in an implicit way is adopted in this work describes the components integrated to the source code ofand it is about an efficient mechanism in the context of the middleware such as Local Agent, Schedule Agent, Filtertelevision environment, where the information input is Agent and Data Agent. The second part describes the newperformed through remote control, a device that was not component added to the STB: Sqlite [13], a C library whichdesigned to this purpose. implements an attached relational database. Figure 2In [11], the AVATAR recommendation system is presented, presents the Recommender module architecture.compatible to the European MHP middleware. The authors I. Implemented Modulespropose a new approach, where the recommendation systemis distributed by broadcast service providers, as well as an This subsection describes the modules added to the Ginga-interactive application. According to the authors, this NCL middleware source code and the extensionsapproach allows the user to choose among different implemented to provide a better connection betweenrecommendation systems, what is not possible when we middleware and the recommendation system.have an STB with a recommendation system installed in Local Agent is the module responsible for constantplant. The AVATAR system uses the approach of implicit monitoring of the remote control. Any interaction betweencollection of user profile and proposes modifications in the the viewer and the control is detected and stored in theMHP middleware to include the monitoring method. The database. The Local Agent is essential for theNaïve Bayes [12] is used as a classification algorithm and recommendation system that uses implicit approach toone of the main reasons for that is the low use of STB perform the profile.resources. Scheduler Agent is the module responsible for periodically SYSTEM OVERVIEW request the data mining. Data mining is a process that demands time and processing, making its executionThe recommendation system proposed in this paper is based impracticable every time the viewer requests aon Ginga middleware. As mentioned before, the version recommendation. Scheduler Agent module guarantees a newused was the open source version of Ginga-NCL processing every 24 hours preferably at night, when the STBmiddleware. Figure 1 presents its architecture consisting of is in standby.three layers:Resident applications responsible for the exhibition(frequently called presentation layer); Ginga Common Core,978-1-4244-6262-9/10/$26.00 ©2010 IEEE October 27 - 30, 2010, Washington, DC 40th ASEE/IEEE Frontiers in Education Conference S1E-3
  4. 4. Session S1E items. For example, the system can be used to create a top- 10 question topic; the students would classify extra material with a grade and the best extra materials would be recommended. It would be also possible to have a top-10 favorite and a top-10 best students. Moreover, the system could also provide a way to look for old content interesting for the user to improve what is being studied at that moment. METHODOLOGY AND TESTS User history and EPG data are necessary to perform the tests. These data were provided by IBOPE (Brazilian Institute of Public Opinion and Statistics) [14] through a treatment process almost entirely manual in order to be in accordance to the standard format which must be used in the Brazilian digital TV system and also in the tests. Many technologies have been arising with the aim at identifying behavior standards and its application in the FIGURE 2 – RECOMMENDER MODULE ARCHITECTURE personalization. The recommendation systems operation is found on these techniques and the most used are the Collaborative Filtering and Content-Based Filtering whichMining Agent is the module that accesses the information in includes several algorithms for each one. Athe viewer’s behavior background and the programming data recommendation system can use only one technique or twofrom the EIT and SDT tables stored in cache to perform the together, becoming a hybrid system.data mining. In order to process the data mining, the Mining In order to study, analyze and choose an algorithm to bemodule has direct access to the database and recovers the used in Technical module, some information filteringTV viewer’s behavior background. From the point of view algorithms were tested. The tests were performed in threeof the system performance, this communication between steps. In the first step, tests were performed with Apriorimining module and user database is important. Without this algorithm. In the second step, the forecast method was used,communication, it would be necessary to implement a new applying Cosine as measure of similarity. The third step wasmodule responsible for recover the database information and to compare the results and the operation with boththen make such data available to the mining algorithm. The algorithms, analyzing the facilities and difficulties,second data set necessary to make possible the data mining especially for the implementation.is the program guide. The program guide is composed by The association techniques algorithms identifyinformation sent by providers through EIT and SDT tables. associations between the data registers which are related inThese tables are stored in cache and are available to be some way. The basic premise finds elements which implyrecovered and processed by the Mining module. Ginga-NCL the presence of others in a same operation aiming atMiddleware does not implement storage mechanism in cache determining which are related. The association rulesof EIT and SDT tables. This functionality was implemented interconnect objects trying to show characteristics andby the Recommender system. tendencies. The association discoveries present trivial and non trivial association. The data was adapted in order to beFilter Agent & Data Agent The raw data returned by the used in Apriori algorithm, that is, it was submitted to a pre-Mining Agent module need to be filtered and later stored in processing phase. The user history was created from IBOPEthe viewer’s database. The Filter Agent and Data Agent data. For the implementation, it is not necessary that the datamodules are responsible for this function. The Filter Agent go through adjustments, as it will be collected in the correctmodule receives the data from the mining provided by the format to be used. The results were satisfactory verifyingMining Agent and eliminates any information that is not that Apriori can be applied to the system for it can beimportant keeping only those which are relevant to the adapted to the system needs. [15, 16]recommendation system such as the name of the program, The Cosine is a similarity measure, a forecast methodtime, date, service provider and the name of the service. The which calculates the similarity between items and users,Data Agent module receives the recommendations and stores consults similar items to a given item and matches itemthem in the viewer’s database. content and user profile. The data also had to be adjusted to be used with Cosine. Database in sqlite was used with theIf there were many educative programs on open TV, it EPG and the user history. From these two tables, it waswould be very useful to recommend other educative possible to derive two more, one with the profile of theprograms. However, “educative” is one of the many TV program watched by the user and other with the profile ofprogram categories. The system can be used inside a genres. It was necessary that the EPG passed through adistance learning system to recommend several types of modification which should also occur in the implementation.978-1-4244-6262-9/10/$26.00 ©2010 IEEE October 27 - 30, 2010, Washington, DC 40th ASEE/IEEE Frontiers in Education Conference S1E-4
  5. 5. Session S1EA new table was created, identical to the EPG table, butadded with fields containing the genres names. According tothe adjustment of the program in the genres, these fieldswere populated with 0 or 1, becoming a matrix. From thesetables it was possible to find the Cosine for the programs andgenres, the profile and what could be recommended to theuser. The results from the Cosine were also satisfactoryconfirming that this technique can be applied to the systemfor it can be adjusted to the system needs. [17, 18] ANALYSIS During the tests, it was possible to note someparticularities. Our system recommends contents based onthe programs genres and our analyses were performedaccording to this standard. With Apriori algorithm, the dataare collected in the correct format to be used. For the Cosine, FIGURE 3. ACCURACY OF THE RECOMMENDATION SYSTEMthe EPG needs to be changed to a matrix before starting theprocess of discovering profiles and recommendations. Figure 3 presents the results obtained after 4 weeks of In a desktop, the feedback of the Cosine calculation is monitoring considering the best value obtained among the 8faster in relation to the feedback of Apriori association rules. schools analyzed. It is clear that on the first weeks, as theHowever, further studies about these algorithms processing collected data were few, Apriori algorithm did not extractin these devices are still being performed. Apriori is able to relevant information from the preferences of the group. Withdiscover the profile from the standards, but to select the the data increase in the visualization background on the thirdprograms to be recommended, another technique must be and fourth week, the algorithm obtained better results andused and the Cosine can find both the profile and the the index of recommendation acceptance increased.recommendations. The Cosine cannot discover these characteristics, butreaches our goal. In order to discover behaviors similar tothe association rules, it is necessary to consult the databank.Apriori output must be operated in order to give the correctuser profile, that is, the rules must be understood, and that isvery hard concerning implementation. The Cosine output isclearer; the result straightly reaches intended goal, allowingthe output to be used without the need of a post-treatment. Regarding the input, there is no need of treatment forApriori, since all data will be used as they are collected.However, for the Cosine, whenever the EPG is updated, thetable containing the EPG matrix must be changed accordingto the new EPG, becoming something hard to work. Theprofile of the genres founded by both algorithms is similar. RESULTSIn order to measure the evolution of the recommendation FIGURE 4 ACCURACY OF THE RECOMMENDATION SYSTEM PER SCHOOLoffered to the students viewer, the following formula wasapplied: Figure 4 presents the accuracy per school. The main Ef = (α / β) 100 (1)  characteristic of the schools is the socioeconomics difference among them. The conclusion is that Apriori algorithm had a Where Ef is the efficacy of the recommendation system, good performance unrestricted to the students’ranging from 0 to 100, α is the recommendation number ´socioeconomic profile.accepted by the students viewers and β is the number ofrecommendation presented. In order to monitor these dataprovided by IBOPE were used. The validation adopted anaccuracy formula presented in (1).978-1-4244-6262-9/10/$26.00 ©2010 IEEE October 27 - 30, 2010, Washington, DC 40th ASEE/IEEE Frontiers in Education Conference S1E-5
  6. 6. Session S1E [3] Souza Filho, G. L., Leite, L. E. C., Batista, C. E. C. F. 2007, “Ginga-J: The Procedural Middleware for the Brazilian Digital TV System.”, Journal of the Brazilian Computer Society, v. 12, n. 4, p. 47-56, March 2007. [4] Ginga-NCL Virtual STB, (March 2009), Available at: http://www.ncl.org.br/ferramentas/index_30.html. [5] Silva, Dirceu et al. Possibilidades educativas e de inclusão social e digital com a TVDi: uma breve análise do cenário brasileiro. Universidade Estadual de Campinas, Brasil. Available in: http://www.google.com.br/url?sa=t&source=web&ct=res&cd=1&ved =0CAYQFjAA&url=http%3A%2F%2Fwww.rieoei.org%2Fdeloslecto res%2F2907Veraszto.pdf&ei=4ar5S- eeM8imuAeA1PG9Dg&usg=AFQjCNHaebMawhyd- xbrBsw0JjSHbTr7LQ&sig2=VGSGkTroJZu8t3NYiHA9_A. Acess in 2010-05-20. [6] Fort, Mônica Cristine. Televisão + Educação = Televisão Educativa. Available in: http://www.google.com.br/url?sa=t&source=web&ct=res&cd=4&ved =0CBUQFjAD&url=http%3A%2F%2Fencipecom.metodista.br%2Fm ediawiki%2Fimages%2Fb%2Fbf%2FGT10_- _008.pdf&ei=N8n5S5TaDYmHuAfp5Py9Dg&usg=AFQjCNFLgg9ng 2elo7UJAjo9dpf8-3I4hg&sig2=RrpI9k-iotN31hCBrc6yNw. Acess in FIGURE 5 – RECOMMENDERTV SYSTEM 2010-05-20. [7] S. H. Hsu, M. H. Wen, H. C. Lin, C. C. Lee, C. H. Lee. 2007. Figure 5 shows Recommender system. The application “AIMED – A personalized TV Recommendation System” in Proc 2007 Interactive TV: A Shared Experience. 5th European Conference,used as front-end is written in NCL and allows the students EuroITV 2007, Amsterdam, the Netherlands.to search the recommendation list selecting the education [8] Vildjiounaite, E., Kyllonen, V., Hannula, T. and Alahuhta, P. 2008.program. Unobtrusive Dynamic Modelling of TV Program Preferences. In Proceedings of the Changing Television Environments, 6th European CONCLUSION Conference, EuroITV 2008, pages 82-91. [9] Zhiwen, Y., Xingshe, Z., Yanbin, H. and Jianhua, G. 2006. TV With the appearance of digital TV, a variety of new program recommendation for multiple viewers based on user profileservices (in the analogical system, channels) will be merging. In Proceedings of the User Modeling and User-Adaptedavailable. This information overload requires the Interaction, pages 63-82. Publishing Springer Netherlands.implementation of new mechanisms to offer facilities to the [10] O’Sullivan, D., Smyth, B., Wilson, D. C., McDonald, K. and Smeaton, A. 2004. Interactive Television Personalization: Fromstudents looking for their education programs. These new Guides to Programs. Personalized Digital Television: Targetingmechanisms suggesting the viewers programs are known as Programs to Individual Viewers. L. Ardissono, A. .Kobsa and M.recommendation systems. A recommendation system Maybury editors, pages 73-91, Kluwer Academic Publisherscompatible with Ginga middleware is presented in this paper [11] Blanco-Fernandez, Y., Pazos-Arias, J., Gil-Solla, A., Ramos-Cabrer,and it is implemented according to the standards of the M.,Lopes-Nores, M., Barragans-Martinez, B. 2005. AVATAR: a Multi-agent TV Recommender System Usingdigital television Brazilian system. The recommendation MHP Applications. In: IEEE International Conference on E-system was modeled considering the current characteristics Technology, E-Commerce and E-Service (EEE 05), pp. 660-665.of the television, and this model can be adjusted to other [12] Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda,standards and also to new portable devices which will be on H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z., Steinbach, M., Hand, D. J., and Steinberg, D. 2007. Top 10 algorithms in datathe market. At last, future works can include algorithms of mining. Knowl. Inf. Syst. 14, 1 (Dec. 2007), 1-37. DOI=collaborative filtering and also a new architecture using http://dx.doi.org/10.1007/s10115-007-0114-2client-server, providing and offering other kinds of [13] Sqlite, (March 2010) Available at: http://www.sqlite.org/.personalization services for the users. [14] “IBOPE”. Available in: http://www.ibope.com.br. Access in December 2009. ACKNOWLEDGMENT [15] Witten, I. H.; Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann, 525 pages, We thank IBOPE for providing real data about the June 2005.electronic program guide and also the viewer’s behavior data [16] Gatto, Elaine C.; Zorzo, Sergio D. “Sistema de Recomendação parafrom March, 05, 2009 to March, 19, 2009. TVDPI,” in 8th International Information and Telecommunication Technologies Symposium. Florianópolis, Santa Catarina, Brasil. 09- REFERENCES 11/12/2009. [17] Torres, Roberto. “Personalização na Internet.” Novatec Editora. 2004.[1] H. Zhang, S. Zheng and J. Yuan. 2005, “A Personalized TV Guide 158p. System Compliant with MHP”, IEEE Transactions on Consumer [18] Gatto, Elaine C.; Zorzo, Sergio D. Application of recommendation Electronics, pages 731-737, Vol. 51, No. 2, MAY 2005. techniques for Brazilian Portable Interactive Digital TV. In: IWSSIP[2] L.F.G. Soares, R.F. Rodrigues, M.F. Moreno. 2007, “Ginga-NCL: The 2010 - 17th International Conference on Systems, Signals and Image declarative Environment of the Brazilian Digital TV System”, Journal Processing. June 17-19, 2010, Rio de Janeiro, Brazil. of the Brazilian Computer Society. V.12, n.4, p.37-46, March 2007.978-1-4244-6262-9/10/$26.00 ©2010 IEEE October 27 - 30, 2010, Washington, DC 40th ASEE/IEEE Frontiers in Education Conference S1E-6

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